from sklearn_benchmarks.reporting.hp_match import HpMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
reporting = HpMatchReporting(against_lib="sklearnex", config="config.yml", log_scale=True)
reporting.make_report()
We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup
time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.
KNeighborsClassifier_brute_force¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: algorithm=brute.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | algorithm | n_jobs | n_neighbors | accuracy_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | accuracy_score_sklearnex | speedup | std_speedup | diff_accuracy_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | e70d8e7886766d41f30889506baa1e67 | 2cf49829391aa7891e877f2ed070adf0 | 1.783428 | 0.103273 | NaN | 0.000449 | 0.001783 | brute | -1 | 1 | 0.663 | 0.166161 | 0.002700 | 0.687 | 10.733113 | 10.734530 | 0.024 |
| 4 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 22d5eac4a4146149b35eae6914921514 | 2cf49829391aa7891e877f2ed070adf0 | 2.598966 | 0.019955 | NaN | 0.000308 | 0.002599 | brute | -1 | 5 | 0.757 | 0.166673 | 0.000619 | 0.742 | 15.593218 | 15.593326 | 0.015 |
| 7 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 16f94fc93942ff7ece860c9b0f64645f | 2cf49829391aa7891e877f2ed070adf0 | 1.949392 | 0.005666 | NaN | 0.000410 | 0.001949 | brute | 1 | 100 | 0.882 | 0.203702 | 0.001211 | 0.875 | 9.569806 | 9.569975 | 0.007 |
| 8 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 16f94fc93942ff7ece860c9b0f64645f | 2cf49829391aa7891e877f2ed070adf0 | 0.018911 | 0.000229 | NaN | 0.000042 | 0.018911 | brute | 1 | 100 | 1.000 | 0.007574 | 0.000126 | 0.000 | 2.496761 | 2.497104 | 1.000 |
| 10 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2d5f2b2c02d77766434d9e5033b4a76c | 2cf49829391aa7891e877f2ed070adf0 | 2.601173 | 0.030045 | NaN | 0.000308 | 0.002601 | brute | -1 | 100 | 0.882 | 0.203260 | 0.000372 | 0.875 | 12.797261 | 12.797283 | 0.007 |
| 11 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 2d5f2b2c02d77766434d9e5033b4a76c | 2cf49829391aa7891e877f2ed070adf0 | 0.022121 | 0.001863 | NaN | 0.000036 | 0.022121 | brute | -1 | 100 | 1.000 | 0.007680 | 0.000881 | 0.000 | 2.880360 | 2.899244 | 1.000 |
| 13 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | efac583623711d395ddae7a1e881ff68 | 2cf49829391aa7891e877f2ed070adf0 | 1.951491 | 0.003758 | NaN | 0.000410 | 0.001951 | brute | 1 | 5 | 0.757 | 0.166382 | 0.000154 | 0.742 | 11.728976 | 11.728981 | 0.015 |
| 16 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | b79d63bb7670c492de5c3befac58fe29 | 2cf49829391aa7891e877f2ed070adf0 | 1.075387 | 0.001489 | NaN | 0.000744 | 0.001075 | brute | 1 | 1 | 0.663 | 0.165224 | 0.000455 | 0.687 | 6.508672 | 6.508697 | 0.024 |
| 19 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | e70d8e7886766d41f30889506baa1e67 | 88843f54689e3271092f70126e1de585 | 1.590791 | 0.025237 | NaN | 0.000010 | 0.001591 | brute | -1 | 1 | 0.896 | 0.025468 | 0.000160 | 0.967 | 62.461386 | 62.462622 | 0.071 |
| 22 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 22d5eac4a4146149b35eae6914921514 | 88843f54689e3271092f70126e1de585 | 2.381358 | 0.080133 | NaN | 0.000007 | 0.002381 | brute | -1 | 5 | 0.922 | 0.026703 | 0.000093 | 0.974 | 89.177914 | 89.178461 | 0.052 |
| 25 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 16f94fc93942ff7ece860c9b0f64645f | 88843f54689e3271092f70126e1de585 | 1.871024 | 0.002125 | NaN | 0.000009 | 0.001871 | brute | 1 | 100 | 0.929 | 0.059930 | 0.001845 | 0.975 | 31.220029 | 31.234820 | 0.046 |
| 28 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 2d5f2b2c02d77766434d9e5033b4a76c | 88843f54689e3271092f70126e1de585 | 2.492094 | 0.032812 | NaN | 0.000006 | 0.002492 | brute | -1 | 100 | 0.929 | 0.059388 | 0.000171 | 0.975 | 41.963136 | 41.963311 | 0.046 |
| 31 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | efac583623711d395ddae7a1e881ff68 | 88843f54689e3271092f70126e1de585 | 1.844899 | 0.001320 | NaN | 0.000009 | 0.001845 | brute | 1 | 5 | 0.922 | 0.026615 | 0.000164 | 0.974 | 69.317442 | 69.318753 | 0.052 |
| 34 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | b79d63bb7670c492de5c3befac58fe29 | 88843f54689e3271092f70126e1de585 | 0.975091 | 0.017602 | NaN | 0.000016 | 0.000975 | brute | 1 | 1 | 0.896 | 0.025576 | 0.000084 | 0.967 | 38.125586 | 38.125793 | 0.071 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.011 | 0.0 | 7.374 | 0.0 | -1 | 1 | 0.044 | 0.003 | 0.245 | 0.245 | See | See |
| 3 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.011 | 0.0 | 7.396 | 0.0 | -1 | 5 | 0.043 | 0.000 | 0.251 | 0.251 | See | See |
| 6 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.011 | 0.0 | 7.447 | 0.0 | 1 | 100 | 0.043 | 0.000 | 0.249 | 0.250 | See | See |
| 9 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.011 | 0.0 | 7.413 | 0.0 | -1 | 100 | 0.043 | 0.000 | 0.251 | 0.251 | See | See |
| 12 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.011 | 0.0 | 7.268 | 0.0 | 1 | 5 | 0.043 | 0.000 | 0.253 | 0.253 | See | See |
| 15 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.011 | 0.0 | 7.441 | 0.0 | 1 | 1 | 0.043 | 0.001 | 0.249 | 0.249 | See | See |
| 18 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.004 | 0.0 | 0.380 | 0.0 | -1 | 1 | 0.008 | 0.000 | 0.514 | 0.514 | See | See |
| 21 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.004 | 0.0 | 0.370 | 0.0 | -1 | 5 | 0.008 | 0.000 | 0.527 | 0.527 | See | See |
| 24 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.004 | 0.0 | 0.379 | 0.0 | 1 | 100 | 0.008 | 0.000 | 0.508 | 0.508 | See | See |
| 27 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.004 | 0.0 | 0.377 | 0.0 | -1 | 100 | 0.008 | 0.000 | 0.517 | 0.517 | See | See |
| 30 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.004 | 0.0 | 0.384 | 0.0 | 1 | 5 | 0.008 | 0.000 | 0.510 | 0.510 | See | See |
| 33 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.004 | 0.0 | 0.382 | 0.0 | 1 | 1 | 0.008 | 0.000 | 0.511 | 0.511 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 1.783 | 0.103 | 0.000 | 0.002 | -1 | 1 | 0.166 | 0.003 | 10.733 | 10.735 | See | See |
| 2 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.021 | 0.002 | 0.000 | 0.021 | -1 | 1 | 0.007 | 0.000 | 2.856 | 2.857 | See | See |
| 4 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2.599 | 0.020 | 0.000 | 0.003 | -1 | 5 | 0.167 | 0.001 | 15.593 | 15.593 | See | See |
| 5 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.024 | 0.004 | 0.000 | 0.024 | -1 | 5 | 0.007 | 0.000 | 3.141 | 3.141 | See | See |
| 7 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 1.949 | 0.006 | 0.000 | 0.002 | 1 | 100 | 0.204 | 0.001 | 9.570 | 9.570 | See | See |
| 8 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.019 | 0.000 | 0.000 | 0.019 | 1 | 100 | 0.008 | 0.000 | 2.497 | 2.497 | See | See |
| 10 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2.601 | 0.030 | 0.000 | 0.003 | -1 | 100 | 0.203 | 0.000 | 12.797 | 12.797 | See | See |
| 11 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.022 | 0.002 | 0.000 | 0.022 | -1 | 100 | 0.008 | 0.001 | 2.880 | 2.899 | See | See |
| 13 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 1.951 | 0.004 | 0.000 | 0.002 | 1 | 5 | 0.166 | 0.000 | 11.729 | 11.729 | See | See |
| 14 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.019 | 0.000 | 0.000 | 0.019 | 1 | 5 | 0.007 | 0.000 | 2.586 | 2.586 | See | See |
| 16 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 1.075 | 0.001 | 0.001 | 0.001 | 1 | 1 | 0.165 | 0.000 | 6.509 | 6.509 | See | See |
| 17 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.018 | 0.000 | 0.000 | 0.018 | 1 | 1 | 0.007 | 0.000 | 2.416 | 2.417 | See | See |
| 19 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 1.591 | 0.025 | 0.000 | 0.002 | -1 | 1 | 0.025 | 0.000 | 62.461 | 62.463 | See | See |
| 20 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.004 | 0.001 | 0.000 | 0.004 | -1 | 1 | 0.001 | 0.000 | 6.896 | 6.908 | See | See |
| 22 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 2.381 | 0.080 | 0.000 | 0.002 | -1 | 5 | 0.027 | 0.000 | 89.178 | 89.178 | See | See |
| 23 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.007 | 0.005 | 0.000 | 0.007 | -1 | 5 | 0.001 | 0.000 | 10.434 | 10.483 | See | See |
| 25 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 1.871 | 0.002 | 0.000 | 0.002 | 1 | 100 | 0.060 | 0.002 | 31.220 | 31.235 | See | See |
| 26 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.003 | 0.000 | 0.000 | 0.003 | 1 | 100 | 0.001 | 0.000 | 3.925 | 3.940 | See | See |
| 28 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 2.492 | 0.033 | 0.000 | 0.002 | -1 | 100 | 0.059 | 0.000 | 41.963 | 41.963 | See | See |
| 29 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.006 | 0.004 | 0.000 | 0.006 | -1 | 100 | 0.001 | 0.000 | 8.423 | 8.445 | See | See |
| 31 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 1.845 | 0.001 | 0.000 | 0.002 | 1 | 5 | 0.027 | 0.000 | 69.317 | 69.319 | See | See |
| 32 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.003 | 0.000 | 0.000 | 0.003 | 1 | 5 | 0.001 | 0.000 | 4.434 | 4.447 | See | See |
| 34 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 0.975 | 0.018 | 0.000 | 0.001 | 1 | 1 | 0.026 | 0.000 | 38.126 | 38.126 | See | See |
| 35 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | 1 | 1 | 0.001 | 0.000 | 2.710 | 2.722 | See | See |
KNeighborsClassifier_kd_tree¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: algorithm=kd_tree.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | algorithm | n_jobs | n_neighbors | accuracy_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | accuracy_score_sklearnex | speedup | std_speedup | diff_accuracy_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | fe8267e25dadb302ed498e50fabf7ef4 | 1eb6cf1a720a225efb91df0b529b0510 | 0.750415 | 1.013010 | NaN | 0.000107 | 0.000750 | kd_tree | -1 | 1 | 0.929 | 0.094522 | 0.001854 | 0.910 | 7.939017 | 7.940544 | 0.019 |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | dc74b969bf622bc24ba3bc62c980983b | 1eb6cf1a720a225efb91df0b529b0510 | 0.980333 | 0.344856 | NaN | 0.000082 | 0.000980 | kd_tree | -1 | 5 | 0.946 | 0.173177 | 0.002969 | 0.941 | 5.660885 | 5.661717 | 0.005 |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 41accfcdcd5c50784bedf6164b63de99 | 1eb6cf1a720a225efb91df0b529b0510 | 4.806605 | 0.401040 | NaN | 0.000017 | 0.004807 | kd_tree | 1 | 100 | 0.951 | 0.511724 | 0.005975 | 0.940 | 9.392964 | 9.393604 | 0.011 |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 022f7445d43bb1dbc24dc3106c03cb93 | 1eb6cf1a720a225efb91df0b529b0510 | 2.817059 | 0.201310 | NaN | 0.000028 | 0.002817 | kd_tree | -1 | 100 | 0.951 | 0.505033 | 0.005633 | 0.940 | 5.577972 | 5.578318 | 0.011 |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 84622ba45e941db642965553529e1941 | 1eb6cf1a720a225efb91df0b529b0510 | 1.502982 | 0.268727 | NaN | 0.000053 | 0.001503 | kd_tree | 1 | 5 | 0.946 | 0.174676 | 0.002490 | 0.941 | 8.604387 | 8.605261 | 0.005 |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | a9455565db1d8e052a783317c99744ff | 1eb6cf1a720a225efb91df0b529b0510 | 0.831806 | 0.334570 | NaN | 0.000096 | 0.000832 | kd_tree | 1 | 1 | 0.929 | 0.094545 | 0.003628 | 0.910 | 8.797947 | 8.804424 | 0.019 |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | fe8267e25dadb302ed498e50fabf7ef4 | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.026699 | 0.015512 | NaN | 0.000599 | 0.000027 | kd_tree | -1 | 1 | 0.891 | 0.000389 | 0.000045 | 0.879 | 68.557773 | 69.010563 | 0.012 |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | dc74b969bf622bc24ba3bc62c980983b | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.022506 | 0.000939 | NaN | 0.000711 | 0.000023 | kd_tree | -1 | 5 | 0.911 | 0.000648 | 0.000010 | 0.905 | 34.734314 | 34.738774 | 0.006 |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 41accfcdcd5c50784bedf6164b63de99 | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.035048 | 0.008406 | NaN | 0.000457 | 0.000035 | kd_tree | 1 | 100 | 0.894 | 0.004464 | 0.000048 | 0.917 | 7.850667 | 7.851114 | 0.023 |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 022f7445d43bb1dbc24dc3106c03cb93 | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.036923 | 0.004802 | NaN | 0.000433 | 0.000037 | kd_tree | -1 | 100 | 0.894 | 0.005557 | 0.001693 | 0.917 | 6.644180 | 6.945728 | 0.023 |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 84622ba45e941db642965553529e1941 | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.019788 | 0.000118 | NaN | 0.000809 | 0.000020 | kd_tree | 1 | 5 | 0.911 | 0.000661 | 0.000016 | 0.905 | 29.954087 | 29.963083 | 0.006 |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | a9455565db1d8e052a783317c99744ff | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.018422 | 0.000183 | NaN | 0.000869 | 0.000018 | kd_tree | 1 | 1 | 0.891 | 0.000372 | 0.000028 | 0.879 | 49.578292 | 49.722076 | 0.012 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 2.743 | 0.043 | 0.029 | 0.0 | -1 | 1 | 0.703 | 0.103 | 3.900 | 3.942 | See | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.287 | 0.057 | 0.024 | 0.0 | -1 | 5 | 0.680 | 0.016 | 4.834 | 4.835 | See | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.263 | 0.035 | 0.025 | 0.0 | 1 | 100 | 0.655 | 0.008 | 4.980 | 4.980 | See | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.354 | 0.077 | 0.024 | 0.0 | -1 | 100 | 0.673 | 0.016 | 4.986 | 4.988 | See | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.337 | 0.035 | 0.024 | 0.0 | 1 | 5 | 0.665 | 0.010 | 5.019 | 5.020 | See | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.247 | 0.046 | 0.025 | 0.0 | 1 | 1 | 0.680 | 0.017 | 4.775 | 4.776 | See | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.002 | 0.016 | 0.0 | -1 | 1 | 0.005 | 0.004 | 0.209 | 0.263 | See | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.031 | 0.0 | -1 | 5 | 0.001 | 0.001 | 0.381 | 0.562 | See | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.032 | 0.0 | 1 | 100 | 0.001 | 0.001 | 0.470 | 0.528 | See | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.032 | 0.0 | -1 | 100 | 0.001 | 0.000 | 0.656 | 0.656 | See | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.031 | 0.0 | 1 | 5 | 0.001 | 0.000 | 0.645 | 0.646 | See | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.032 | 0.0 | 1 | 1 | 0.001 | 0.000 | 0.664 | 0.664 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 0.750 | 1.013 | 0.000 | 0.001 | -1 | 1 | 0.095 | 0.002 | 7.939 | 7.941 | See | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 1 | 0.000 | 0.000 | 9.894 | 10.187 | See | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 0.980 | 0.345 | 0.000 | 0.001 | -1 | 5 | 0.173 | 0.003 | 5.661 | 5.662 | See | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 5 | 0.000 | 0.000 | 8.272 | 8.592 | See | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 4.807 | 0.401 | 0.000 | 0.005 | 1 | 100 | 0.512 | 0.006 | 9.393 | 9.394 | See | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.003 | 0.001 | 0.000 | 0.003 | 1 | 100 | 0.001 | 0.000 | 4.576 | 4.724 | See | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 2.817 | 0.201 | 0.000 | 0.003 | -1 | 100 | 0.505 | 0.006 | 5.578 | 5.578 | See | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.005 | 0.001 | 0.000 | 0.005 | -1 | 100 | 0.001 | 0.000 | 6.651 | 6.827 | See | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 1.503 | 0.269 | 0.000 | 0.002 | 1 | 5 | 0.175 | 0.002 | 8.604 | 8.605 | See | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 0.000 | 0.000 | 3.902 | 4.081 | See | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 0.832 | 0.335 | 0.000 | 0.001 | 1 | 1 | 0.095 | 0.004 | 8.798 | 8.804 | See | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 0.000 | 0.000 | 4.026 | 4.233 | See | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.027 | 0.016 | 0.001 | 0.000 | -1 | 1 | 0.000 | 0.000 | 68.558 | 69.011 | See | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 1 | 0.000 | 0.000 | 22.720 | 23.588 | See | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.023 | 0.001 | 0.001 | 0.000 | -1 | 5 | 0.001 | 0.000 | 34.734 | 34.739 | See | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 5 | 0.000 | 0.000 | 25.066 | 26.077 | See | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.035 | 0.008 | 0.000 | 0.000 | 1 | 100 | 0.004 | 0.000 | 7.851 | 7.851 | See | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 0.000 | 0.000 | 6.204 | 6.490 | See | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.037 | 0.005 | 0.000 | 0.000 | -1 | 100 | 0.006 | 0.002 | 6.644 | 6.946 | See | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 100 | 0.000 | 0.000 | 22.524 | 23.727 | See | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.020 | 0.000 | 0.001 | 0.000 | 1 | 5 | 0.001 | 0.000 | 29.954 | 29.963 | See | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 0.000 | 0.000 | 6.035 | 6.429 | See | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.018 | 0.000 | 0.001 | 0.000 | 1 | 1 | 0.000 | 0.000 | 49.578 | 49.722 | See | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 0.000 | 0.000 | 6.306 | 6.587 | See | See |
KMeans_tall¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 0.496 | 0.074 | 30 | 0.032 | 0.0 | random | 0.373 | 0.025 | 1.328 | 1.331 | See | See |
| 3 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 0.534 | 0.015 | 30 | 0.030 | 0.0 | k-means++ | 0.402 | 0.025 | 1.327 | 1.330 | See | See |
| 6 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 5.145 | 0.218 | 30 | 0.155 | 0.0 | random | 2.470 | 0.052 | 2.083 | 2.084 | See | See |
| 9 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 5.273 | 0.018 | 30 | 0.152 | 0.0 | k-means++ | 2.763 | 0.074 | 1.908 | 1.909 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KMeans_tall | predict | 1000000 | 1000 | 2 | 0.002 | 0.000 | 30 | 0.010 | 0.000 | random | 0.0 | 0.0 | 5.781 | 11.830 | See | See |
| 2 | KMeans_tall | predict | 1000000 | 1 | 2 | 0.001 | 0.000 | 30 | 0.000 | 0.001 | random | 0.0 | 0.0 | 7.894 | 14.007 | See | See |
| 4 | KMeans_tall | predict | 1000000 | 1000 | 2 | 0.002 | 0.001 | 30 | 0.010 | 0.000 | k-means++ | 0.0 | 0.0 | 13.461 | 15.063 | See | See |
| 5 | KMeans_tall | predict | 1000000 | 1 | 2 | 0.001 | 0.000 | 30 | 0.000 | 0.001 | k-means++ | 0.0 | 0.0 | 14.328 | 14.988 | See | See |
| 7 | KMeans_tall | predict | 1000000 | 1000 | 100 | 0.002 | 0.000 | 30 | 0.461 | 0.000 | random | 0.0 | 0.0 | 7.277 | 7.673 | See | See |
| 8 | KMeans_tall | predict | 1000000 | 1 | 100 | 0.001 | 0.000 | 30 | 0.001 | 0.001 | random | 0.0 | 0.0 | 13.137 | 13.593 | See | See |
| 10 | KMeans_tall | predict | 1000000 | 1000 | 100 | 0.002 | 0.000 | 30 | 0.492 | 0.000 | k-means++ | 0.0 | 0.0 | 6.865 | 7.261 | See | See |
| 11 | KMeans_tall | predict | 1000000 | 1 | 100 | 0.001 | 0.000 | 30 | 0.001 | 0.001 | k-means++ | 0.0 | 0.0 | 12.759 | 13.100 | See | See |
KMeans_short¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | algorithm | init | max_iter | n_clusters | n_init | tol | adjusted_rand_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | adjusted_rand_score_sklearnex | speedup | std_speedup | diff_adjusted_rand_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 3db9c030f488d0e8ff350ae449da4627 | 058b7e3842b587a5c675518f0706f5ee | 0.001809 | 0.000249 | 20 | 0.008846 | 0.000002 | full | random | 20 | 300 | 1 | 1.000000e-16 | 0.000126 | 0.000420 | 0.000022 | -0.000965 | 4.303543 | 4.309574 | 0.001090 |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | ac9b8cec4749455c0d1cd1a92c150716 | 058b7e3842b587a5c675518f0706f5ee | 0.001710 | 0.000074 | 20 | 0.009359 | 0.000002 | full | k-means++ | 20 | 300 | 1 | 1.000000e-16 | 0.001245 | 0.000425 | 0.000031 | -0.000750 | 4.018310 | 4.029129 | 0.001995 |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 3db9c030f488d0e8ff350ae449da4627 | 7f85b913f395ec54101a6738ea63a9a7 | 0.002425 | 0.000102 | 20 | 0.329961 | 0.000002 | full | random | 20 | 300 | 1 | 1.000000e-16 | 0.278733 | 0.000918 | 0.000054 | 0.293767 | 2.640413 | 2.644915 | 0.015034 |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | ac9b8cec4749455c0d1cd1a92c150716 | 7f85b913f395ec54101a6738ea63a9a7 | 0.002428 | 0.000149 | 20 | 0.329529 | 0.000002 | full | k-means++ | 20 | 300 | 1 | 1.000000e-16 | 0.317011 | 0.000925 | 0.000055 | 0.256968 | 2.624866 | 2.629577 | 0.060044 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 10000 | 10000 | 2 | 0.072 | 0.000 | 20 | 0.002 | 0.0 | random | 0.026 | 0.002 | 2.769 | 2.778 | See | See |
| 3 | KMeans_short | fit | 10000 | 10000 | 2 | 0.210 | 0.003 | 20 | 0.001 | 0.0 | k-means++ | 0.080 | 0.000 | 2.624 | 2.624 | See | See |
| 6 | KMeans_short | fit | 10000 | 10000 | 100 | 0.195 | 0.001 | 20 | 0.041 | 0.0 | random | 0.105 | 0.001 | 1.861 | 1.861 | See | See |
| 9 | KMeans_short | fit | 10000 | 10000 | 100 | 0.557 | 0.009 | 20 | 0.014 | 0.0 | k-means++ | 0.307 | 0.002 | 1.813 | 1.813 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 0.002 | 0.0 | 20 | 0.009 | 0.000 | random | 0.000 | 0.0 | 4.304 | 4.310 | See | See |
| 2 | KMeans_short | predict | 10000 | 1 | 2 | 0.001 | 0.0 | 20 | 0.000 | 0.001 | random | 0.000 | 0.0 | 13.664 | 14.014 | See | See |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | 0.002 | 0.0 | 20 | 0.009 | 0.000 | k-means++ | 0.000 | 0.0 | 4.018 | 4.029 | See | See |
| 5 | KMeans_short | predict | 10000 | 1 | 2 | 0.001 | 0.0 | 20 | 0.000 | 0.001 | k-means++ | 0.000 | 0.0 | 12.504 | 12.883 | See | See |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 0.002 | 0.0 | 20 | 0.330 | 0.000 | random | 0.001 | 0.0 | 2.640 | 2.645 | See | See |
| 8 | KMeans_short | predict | 10000 | 1 | 100 | 0.001 | 0.0 | 20 | 0.001 | 0.001 | random | 0.000 | 0.0 | 10.418 | 10.613 | See | See |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | 0.002 | 0.0 | 20 | 0.330 | 0.000 | k-means++ | 0.001 | 0.0 | 2.625 | 2.630 | See | See |
| 11 | KMeans_short | predict | 10000 | 1 | 100 | 0.002 | 0.0 | 20 | 0.001 | 0.002 | k-means++ | 0.000 | 0.0 | 11.523 | 11.712 | See | See |
LogisticRegression¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | C | class_weight | dual | fit_intercept | intercept_scaling | l1_ratio | max_iter | multi_class | n_jobs | penalty | random_state | solver | tol | verbose | warm_start | accuracy_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | accuracy_score_sklearnex | speedup | std_speedup | diff_accuracy_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | f11be2529b0a77bc8fb5ee8de141e6b2 | 09c36ed6521e03974d0c134895f56c01 | 0.000357 | 0.000337 | [20] | 2.242799 | 3.566971e-07 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0001 | 0 | False | 0.56 | 0.000864 | 0.001722 | 0.55 | 0.412965 | 0.921140 | 0.01 |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | f11be2529b0a77bc8fb5ee8de141e6b2 | 7daecaea01e5baa8c862f264e3c1c3b0 | 0.001707 | 0.000445 | [26] | 4.686518 | 1.707024e-05 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0001 | 0 | False | 0.35 | 0.004313 | 0.001317 | 0.28 | 0.395781 | 0.413832 | 0.07 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 1000000 | 1000000 | 100 | 10.159 | 0.554 | [20] | 0.079 | 0.000 | 1.695 | 0.033 | 5.994 | 5.996 | See | See |
| 3 | LogisticRegression | fit | 1000 | 1000 | 10000 | 0.916 | 0.772 | [26] | 0.087 | 0.001 | 0.724 | 0.029 | 1.266 | 1.267 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | 0.000 | 0.0 | [20] | 2.243 | 0.0 | 0.001 | 0.002 | 0.413 | 0.921 | See | See |
| 2 | LogisticRegression | predict | 1000000 | 1 | 100 | 0.000 | 0.0 | [20] | 0.017 | 0.0 | 0.000 | 0.000 | 0.351 | 0.357 | See | See |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | 0.002 | 0.0 | [26] | 4.687 | 0.0 | 0.004 | 0.001 | 0.396 | 0.414 | See | See |
| 5 | LogisticRegression | predict | 1000 | 1 | 10000 | 0.000 | 0.0 | [26] | 1.189 | 0.0 | 0.001 | 0.000 | 0.111 | 0.111 | See | See |
Ridge¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | alpha | copy_X | fit_intercept | max_iter | normalize | random_state | solver | tol | r2_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | r2_score_sklearnex | speedup | std_speedup | diff_r2_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Ridge | predict | 1000 | 1000 | 10000 | a70ae8dc5c059ec88cf27c61016aee2a | 14fae294d530397144c65ba3209bf125 | 0.009451 | 0.000278 | NaN | 8.464364 | 0.000009 | 1.0 | True | True | NaN | deprecated | NaN | auto | 0.001 | 0.082567 | 0.016041 | 0.000363 | 0.122191 | 0.589217 | 0.589368 | 0.039624 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1000 | 1000 | 10000 | 0.164 | 0.003 | 0.488 | 0.0 | 0.170 | 0.001 | 0.967 | 0.967 | See | See |
| 3 | Ridge | fit | 1000000 | 1000000 | 100 | 1.041 | 0.073 | 0.768 | 0.0 | 0.292 | 0.258 | 3.566 | 4.755 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Ridge | predict | 1000 | 1000 | 10000 | 0.009 | 0.0 | 8.464 | 0.0 | 0.016 | 0.0 | 0.589 | 0.589 | See | See |
| 2 | Ridge | predict | 1000 | 1 | 10000 | 0.000 | 0.0 | 1.452 | 0.0 | 0.000 | 0.0 | 0.618 | 0.661 | See | See |
| 4 | Ridge | predict | 1000000 | 1000 | 100 | 0.000 | 0.0 | 6.440 | 0.0 | 0.000 | 0.0 | 0.380 | 0.610 | See | See |
| 5 | Ridge | predict | 1000000 | 1 | 100 | 0.000 | 0.0 | 0.018 | 0.0 | 0.000 | 0.0 | 0.599 | 0.638 | See | See |